AD user-need-miner
用户需求挖掘与画像构建：从用户访谈/社交媒体/评论中提取真实需求，生成结构化画像与需求清单。Invoke when user asks 用户需求、用户画像、需求分析、用户调研、痛点分析.
用户需求挖掘与画像构建：从用户访谈/社交媒体/评论中提取真实需求，生成结构化画像与需求清单。Invoke when user asks 用户需求、用户画像、需求分析、用户调研、痛点分析.
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
Proceduretype and topics are labelled automatically from the skill text
How to improve
For the model run — optional
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 41/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (user-need-miner) differs from the folder (03-user-need-miner)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 21 steps
- 100Execution cost. Instruction body is 246 tokens
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 93: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 9 headings
- +3Step-by-step instructions: 21 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.
External checks
ClawHub: clean
This skill is a straightforward Chinese-language user-research helper, with its main caveat being that it may use web search to gather public user comments when the user has not provided source data.
LLM: benign (high) · VirusTotal: · 9 Aug 2026